Staff Engineer, Machine Learning & Data Infrastructure

About the role

In this role at OKX, you will lead the architecture and development of machine learning and data infrastructure focused on improving risk and fraud prevention systems. You will work on optimizing model performance and building reusable infrastructure for model management across various cloud platforms.

You will be part of a team that values collaboration and technical leadership, where you'll mentor engineers and set the technical direction for projects involving machine learning, data engineering, and cloud infrastructure. Success in this role is measured by your ability to deliver measurable improvements in system performance and reliability.

The salary range for this position is competitive, starting at $214,666 and going up to $321,999.

Skills that matter here

• Python: This role requires strong Python skills for building and optimizing ML infrastructure.
• SQL: SQL expertise is necessary for managing and querying data efficiently.
• Java: Experience with Java or similar languages is essential for backend development.
• Scala: Scala knowledge is important for working with data processing frameworks.
• Go: Familiarity with Go is beneficial for developing scalable systems.
• C++: C++ skills can enhance performance optimization in system-level programming.

Who this role suits

• You have a strong technical background in building distributed systems or data platforms.
• You thrive in collaborative environments and enjoy mentoring others.
• You are adept at turning ambiguous requirements into clear architectural designs.
• You have a hands-on approach to engineering and technical leadership.

From the employer

• Improve model inference latency, throughput, resource utilization, and cost.
• Profile the full prediction path —from feature retrieval and preprocessing to model execution — and deliver measurable improvements without unacceptable model-quality regressions.
• Build reusable infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.
• Design and optimize offline data pipelines on Databricks, Apache Spark, and warehouse/lakehouse technologies, addressing data modeling, partitioning, joins, shuffles, skew, incremental processing, and cost.
• Connect offline and online feature pipelines, ensuring point-in-time correctness, consistent feature definitions, freshness, lineage, and reproducible backfills.
• Evolve reliable feature services and data integrations supporting risk models and decision systems.
• Set technical direction, mentor engineers, and lead delivery across ML, data engineering, cloud infrastructure, security, and product teams.

• A record of building and operating large-scale distributed systems, ML infrastructure, or data platforms, with technical ownership across teams.
• Hands-on experience optimizing production model performance, supported by measurable improvements in latency, throughput, utilization, or cost.
• Deep understanding of Databricks and Spark, or comparable offline data platforms, including execution plans, distributed query optimization, storage layouts, data correctness, and workload cost management.
• Experience with the model lifecycle, including training/serving integration, validation, deployment, observability, and rollback.
• Strong Python, SQL, and at least one backend or systems language such as Java, Scala, Go, or C++.
• Ability to turn ambiguous requirements into clear architecture and deliver maintainable systems through hands-on engineering and technical leadership.

• Competitive total compensation package
• L&D programs for employees' growth and development
• Various team building programs and company events
• Wellness and meal allowances
• Comprehensive healthcare schemes for employees and dependants
• More that we love to tell you along the process!